Survey on Partition based Clustering Algorithms in Big Data

Authors

  • E Mahima Jane Department of Computer Application , Madras Christian College, Tambaram, India
  • E George Dharma Prakash Raj Department of Computer Science and Engineering, Bharathidasan University, Trichy, India

DOI:

https://doi.org/10.26438/ijcse/v5i12.323325

Keywords:

KMeans, PAM, CLARA, CLARANS

Abstract

Clustering is the task of dividing the data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This paper focuses on the traditional partition based clustering algorithms such as KMeans, K Medoids, PAM, CLARA and CLARANS and its advantages and disadvantages.

References

[1] T Saha, K Dhas “ Inregration and Interelation of Bigdata With Cloud Computing: A Review “ International Journal of Computer Sciences and Engineering Vol.5(11), Nov 2017, E-ISSN: 2347-2693

[2] Prateeksha Tomar, Amit Kumar Manjhvar, "Clustering Classification for Diabetic Patients using K-Means and M-Tree prediction model", International Journal of Scientific Research in Multidisciplinary Studies , Vol.3, Issue.6, pp.48-53, 2017.

[3] Shalini S Singh, N C Chauhan,” K-means v/s Kmedoids: A Comparative Study”, National Conference on Recent Trends in Engineering & Technology, May 2011.

[4] C. Zhang, and Z. Fang, An improved k-means clustering algorithm, Journal of Information & Computational Science, 10(1), 2013, 193-199.

[5] 5.Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil A. Zomaya, S. Foufou, and A. Bouras, A Survey of Clustering Algorithms for Big Data:Taxonomy& Empirical Analysis, Accepted for IEEE transaction on emerging topics in computing 2014.

[6] Gopi Gandhi, RohitSrivastava ,”Review Paper: A Comparative Study on Partitioning Techniques of Clustering Algorithms “- International Journal of Computer Applications (0975 – 8887) Volume 87 – No.9, February 2014

[7] AzharRauf, Sheeba, SaeedMahfooz, Shah Khusro and HumaJaved“ “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity “Middle-East Journal of Scientific Research 12 (7): 959-963, 2012

[8] Ali SeyedShirkhorshidi, SaeedAghabozorgi, Teh Ying Wah and TututHerawan, “Big Data Clustering: A Review”, Research Gate, Jun, (2014)

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i12.323325
Published: 2025-11-12

How to Cite

[1]
E. Mahima Jane and E. George Dharma Prakash Raj, “Survey on Partition based Clustering Algorithms in Big Data”, Int. J. Comp. Sci. Eng., vol. 5, no. 12, pp. 323–325, Nov. 2025.

Issue

Vol. 5 No. 12 (2017): IJCSE December Edition

Section

Survey Article

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